Unsupervised Resource Creation for Textual Inference Applications
Jeremy Bensley, Andrew Hickl · 2008
This paper explores how a battery of unsupervised techniques can be used in order to create large, high-quality corpora for textual inference applications, such as systems for recognizing textual entailment (TE) and textual contradiction (TC).We show that it is possible to automatically generate sets of positive and negative instances of textual entailment and contradiction from textual corpora with greater than 90% precision.We describe how we generated more than 1 million TE pairs -and a corresponding set of 500,000 TC pairs -from the documents found in the 2 GB AQUAINT-2 newswire corpus.